Browse Lessons

4 core pieces to MLOps // Ariel Biller // MLOps Coffee Sessions #37 short clip

Law of Diminishing Returns for Running AI Proof-of-Concepts // Oguzhan Gencoglu // MLOps Meetup #62

Tooling: Blessing and Curse? // Geoff Sims // Coffee Sessions #34 short clip

Organisational Challenges of MLOps // Adam Sroka // MLOps Coffee Sessions #38

Is Explainability Table Stakes For An ML Monitoring Solution // Lex Beattie // Meetup short clip

From Idea to Production ML // Lex Beattie - Michael Munn - Mike Moran // MLOps Meetup #61

MLOps Memes // Ariel Biller // MLOps Coffee Sessions #37

Luigi in Production Part 2 // Luigi Patruno // MLOps Coffee Sessions #36

MLOps Community 1 Year Anniversary! // Meetup #59 clip

War Stories Productionising ML // Nick Masca // Coffee Session#35

Who is the right person to interact with in Evaluation Store? // Short clip from Meetup #57

Evaluation Store Discussion // Josh Tobin // Short clip from Meetup #57

Machine Learning at Atlassian // Geoff Sims // Coffee Session#34

Model & Data Drift Deep Dive // Ben Wilson // MLOps Meetup Clips

Underspecification Paper Key Takeaways // D. Sculley // MLOps Clips

Creator of Kubeflow's opinion on a feature store // MLOps Meetup Clips

When to add more complexity in your ML infrastructure? // Daniel Stahl // MLOps Meetup Clips

Start-ups vs Enterprise OSS Strategy in Machine Learning Tooling // MLOps Clips with Sarah Catanzaro

MLOps Investments // Sarah Catanzaro // Coffee Session #33

Model Watching: Keeping Your Project in Production // Ben Wilson // MLOps Meetup #58

A Missing Link in the ML Infrastructure Stack // Josh Tobin // MLOps Meetup #57

Build by Small Pieces // Igor Lushchyk // MLOps Meetup #55 short clip

The Intricacy of the Labeling Process // Laszlo Sragner // MLOps Meetup #54 short clip

The Godfather Of MLOps // D. Sculley // MLOps Coffee Sessions #32

Operationalizing Machine Learning at a Large Financial Institution // Daniel Stahl //MLOps Meetup#56

What’s changed in the MLOps ecosystem? Explained by the Godfather of MLOps // Coffee Session Clip

How to Avoid Suffering in MLOps/Data Engineering Role // Igor Lushchyk // MLOps Meetup #55

Product Management in Machine Learning // Laszlo Sragner // MLOps Meetup #54

Does anyone know of a good way to manage dependencies? // MLOps Coffee Sessions #31 Part 2 Clip

MLOps Engineering Labs Recap // Part 2 // MLOps Coffee Sessions #31

How Explainable AI is Critical to Building Responsible AI // Krishna Gade // MLOps Meetup #53

The Power of Explainable AI // Krishna Gade // MLOps Meetup #53 short clip

MLOps Engineering Labs Recap // Part 1 // MLOps Coffee Sessions #30

'Git for Data' - Who, What, How and Why? // Luke Feeney & Gavin Mendel-Gleason // MLOps Meetup #52

Agile AI Ethics: Balancing Short Term Value with Long Term Ethical Outcomes // Pamela Jasper // #51

Culture and Architecture in MLOps // Jet Basrawi // MLOps Coffee Sessions #29

2 tools = 90% operational ML // Michael Del Balso -Willem Pienaar - David Aronchick // MLOps #50

Machine Learning Design Patterns for MLOps // Valliappa Lakshmanan // MLOps Meetup #49

Lessons Learned From Hosting the ML Engineered Podcast // Charlie You // MLOps Coffee Sessions #28

Practical MLOps // Noah Gift // MLOps Coffee Sessions #27

Serving ML Models at a High Scale with Low Latency // Manoj Agarwal // MLOps Meetup #48

Ep#9 - Private data Data Science friendly

Machine Learning Feature Store Panel Discussion // MLOps Coffee Sessions #26

ProductizeML: Assisting Your Team to Better Build ML Products // Adrià Romero // MLOps Meetup #47

Ep#8 - The revolution of Federated Learning

Most Underrated MLOps Topics // Marian Ignev // MLOps Coffee Sessions #25

Real-time Feature Pipelines, A Personal History // Hendrik Brackmann // MLOps Meetup #46

MLOps Feature Store Explanation